#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>

using namespace std;
using namespace cv;

int main()
{
	
	//-- 读取图像
	Mat img_1 = imread("E://重要文件//slambook-master//ch7//1.png", CV_LOAD_IMAGE_COLOR);
	Mat img_2 = imread("E://重要文件//slambook-master//ch7//2.png", CV_LOAD_IMAGE_COLOR);

	//-- 初始化
	std::vector<KeyPoint> keypoints_1, keypoints_2;
	Mat descriptors_1, descriptors_2;
	Ptr<FeatureDetector> detector = ORB::create();
	Ptr<DescriptorExtractor> descriptor = ORB::create();
	// Ptr<FeatureDetector> detector = FeatureDetector::create(detector_name);
	// Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create(descriptor_name);
	Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");

	//-- 第一步:检测 Oriented FAST 角点位置
	detector->detect(img_1, keypoints_1);
	detector->detect(img_2, keypoints_2);

	//-- 第二步:根据角点位置计算 BRIEF 描述子
	descriptor->compute(img_1, keypoints_1, descriptors_1);
	descriptor->compute(img_2, keypoints_2, descriptors_2);

	Mat outimg1;
	drawKeypoints(img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT);
	imshow("ORB特征点", outimg1);

	//-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
	vector<DMatch> matches;
	//BFMatcher matcher ( NORM_HAMMING );
	matcher->match(descriptors_1, descriptors_2, matches);

	//-- 第四步:匹配点对筛选
	double min_dist = 10000, max_dist = 0;

	//找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
	for (int i = 0; i < descriptors_1.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < min_dist) min_dist = dist;
		if (dist > max_dist) max_dist = dist;
	}

	// 仅供娱乐的写法
	min_dist = min_element(matches.begin(), matches.end(), [](const DMatch& m1, const DMatch& m2) {return m1.distance < m2.distance; })->distance;
	max_dist = max_element(matches.begin(), matches.end(), [](const DMatch& m1, const DMatch& m2) {return m1.distance < m2.distance; })->distance;

	printf("-- Max dist : %f \n", max_dist);
	printf("-- Min dist : %f \n", min_dist);

	//当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
	std::vector< DMatch > good_matches;
	for (int i = 0; i < descriptors_1.rows; i++)
	{
		if (matches[i].distance <= max(2 * min_dist, 30.0))
		{
			good_matches.push_back(matches[i]);
		}
	}

	//-- 第五步:绘制匹配结果
	Mat img_match;
	Mat img_goodmatch;
	drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_match);
	drawMatches(img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch);
	imshow("所有匹配点对", img_match);
	imshow("优化后匹配点对", img_goodmatch);
	waitKey(0);

	return 0;
}
